In this paper we present an information based framework for addressing multi-sensor data fusion and its management. The basis for this approach is the notion of Bayesian Information Update from which we present a probabilistic model for data fusion and its management. We proceed to outline how architectures and algorithms can be derived from the information update. This leads to a framework for sensor management based on using information as the expected utility of taking actions. We show how Fisher information and more generally Entropy can be used to quantify information. We conclude by briefly outlining a vehicle application making use of data fusion algorithms and sensor management techniques that we present.